Insecure Attachment and Technology Addiction Among Young Adults: The Mediating Role of Impulsivity, Alexithymia, and General Psychological Distress
Bibliographic record
Abstract
Previous studies have emphasized the effect of insecurity attachment on youth's Internet and smartphone addiction. In this study, we examine the mediating role of alexithymia, impulsivity, and general psychological distress in the relationship between insecure attachment dimensions and technology addiction. Data were collected from 539 adolescents and young adults, mostly women ( N = 378; 70.1 percent), aged 19.76 ± 1.99 years. Participants completed self-report measures of attachment insecurity, psychological risk factors (i.e., impulsivity, psychological distress, and alexithymia), and technology addiction (i.e., problematic Internet use, smartphone, and Internet addiction). The gender-related (i.e., multi-group) mediation model was tested through a path analysis with both observed and latent variables. Attachment anxiety had no direct effect on technology addiction, whereas attachment avoidance had a small negative direct effect, but only among women. Insecure attachment dimensions were significantly associated with psychological risk factors, whereas the latter had a significant, direct association with technology addiction. Psychological risk factors significantly mediated the association between insecure attachment dimensions and technology addiction. Finally, the tested model was gender-invariant. Findings suggest that insecure attachment dimensions have an indirect effect on the development of technology addiction mediated almost entirely by higher levels of psychological risk factors. Such findings might have relevant implications to inform any treatment plan for young adults who are overinvolved with technology activities and so to deliver patient-tailored interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".